HRS 2016 - Cognition classification

Data

The data used in this analysis comes from the 2016 wave of the HRS study. The data was directly downloaded from their website.

The files used are:

• trk2022tr_r.dta

• H16A_R.dta

• H16D_R.dta

To norm the factor scores, RNJ provided a dataset that identified the participants with normal cognition in the HCAP sample.

Recode demographic variables

This section shows the recoding of the demographic variables.

Age came from the variable PA019 and no recoding was done.

Years of education came from the variable SCHLYRS and no recoding was done. Five participants had their values changed based on RNJ’s code.

Female came from the variable GENDER.

Table 1: Coding of female indicator

Female from trk2022tr_r	GENDER	n
0	1. Male	8664
1	2. Female	12245
NA	NA	3

Black came from the variables RACE and HISPANIC.

Table 2: Coding of Black or African-American indicator

Black or African-American (not Hispanic) from trk2022tr_r	RACE	HISPANIC	n
0	0. Not obtained	0. Not obtained	5
0	0. Not obtained	1. Hispanic, Mexican	40
0	0. Not obtained	2. Hispanic, Other	23
0	0. Not obtained	3. Hispanic, type unknown	1
0	0. Not obtained	5. Non-Hispanic	9
0	1. White/Caucasian	0. Not obtained	9
0	1. White/Caucasian	1. Hispanic, Mexican	1138
0	1. White/Caucasian	2. Hispanic, Other	677
0	1. White/Caucasian	3. Hispanic, type unknown	18
0	1. White/Caucasian	5. Non-Hispanic	11980
0	2. Black or African American	1. Hispanic, Mexican	10
0	2. Black or African American	2. Hispanic, Other	86
0	2. Black or African American	3. Hispanic, type unknown	2
0	7. Other	0. Not obtained	15
0	7. Other	1. Hispanic, Mexican	795
0	7. Other	2. Hispanic, Other	632
0	7. Other	3. Hispanic, type unknown	5
0	7. Other	5. Non-Hispanic	1030
1	2. Black or African American	0. Not obtained	12
1	2. Black or African American	5. Non-Hispanic	4422
NA	NA	NA	3

Hispanic came from the variable HISPANIC.

Table 3: Coding of Hispanic indicator

Hispanic from trk2022tr_r	HISPANIC	n
0	0. Not obtained	41
0	5. Non-Hispanic	17441
1	1. Hispanic, Mexican	1983
1	2. Hispanic, Other	1418
1	3. Hispanic, type unknown	26
NA	NA	3

Recode the functional impairment items

This section shows the recoding of the IADL items.

There are 11 IADL items that are considered to measure functional impairment.

• Item responses of 1 (Yes) and 6 (Can’t do) are recoded as “impairment”

• Item responses of 5 (No), 7 (Don’t do), 8 (Don’t know) and 9 (Refused) are recoded as “no impairment”

Two of the items - “Difficulty taking medication” and “Do you think you would have difficulty taking medication” are logically dependent and are merged into one item

The sum of these 10 recoded IADL items is the functional impairment score

Table 4: ADL: Difficulty Dressing recoding

	ADL: Difficulty Dressing			Total
	Impaired	Not impaired	Unknown
DIFFICULTY- DRESSING
1	2,352	0	0	2,352
5	0	10,875	0	10,875
6	52	0	0	52
7	0	0	12	12
8	0	0	10	10
9	0	0	2	2
Unknown	0	0	7,609	7,609
Total	2,404	10,875	7,633	20,912

Table 5: ADL: Difficulty Bathing recoding

	ADL: Difficulty Bathing			Total
	Impaired	Not impaired	Unknown
DIFFICULTY BATHING
1	1,690	0	0	1,690
5	0	8,552	0	8,552
6	39	0	0	39
7	0	0	6	6
8	0	0	6	6
9	0	0	1	1
Unknown	0	0	10,618	10,618
Total	1,729	8,552	10,631	20,912

Table 6: ADL: Difficulty Eating recoding

	ADL: Difficulty Eating			Total
	Impaired	Not impaired	Unknown
DIFFICULTY EATING
1	806	0	0	806
5	0	9,439	0	9,439
6	28	0	0	28
7	0	0	15	15
8	0	0	4	4
9	0	0	2	2
Unknown	0	0	10,618	10,618
Total	834	9,439	10,639	20,912

Table 7: ADL: Difficulty Using Toilet recoding

	ADL: Difficulty Using Toilet			Total
	Impaired	Not impaired	Unknown
DIFFICULTY USING TOILET
1	1,336	0	0	1,336
5	0	8,892	0	8,892
6	32	0	0	32
7	0	0	23	23
8	0	0	10	10
9	0	0	1	1
Unknown	0	0	10,618	10,618
Total	1,368	8,892	10,652	20,912

Table 8: ADL: Difficulty Using Maps recoding

	IADL: Difficulty Using Maps			Total
	Impaired	Not impaired	Unknown
DIFFICULTY- USING MAPS
1	2,517	0	0	2,517
5	0	16,811	0	16,811
6	268	0	0	268
7	0	0	1,233	1,233
8	0	0	39	39
9	0	0	7	7
Unknown	0	0	37	37
Total	2,785	16,811	1,316	20,912

Table 9: IADL: Difficulty Meal Prep recoding

	IADL: Difficulty Meal Prep			Total
	Impaired	Not impaired	Unknown
IADL MEAL PREPARATION DIFFICULTY
1	1,207	0	0	1,207
5	0	18,880	0	18,880
6	101	0	0	101
7	0	0	679	679
8	0	0	5	5
9	0	0	3	3
Unknown	0	0	37	37
Total	1,308	18,880	724	20,912

Table 10: IADL: Difficulty Grocery Shopping recoding

	IADL: Difficulty Grocery Shopping			Total
	Impaired	Not impaired	Unknown
IADL GROC SHOP DIFFICULTY
1	1,761	0	0	1,761
5	0	18,324	0	18,324
6	81	0	0	81
7	0	0	701	701
8	0	0	6	6
9	0	0	2	2
Unknown	0	0	37	37
Total	1,842	18,324	746	20,912

Table 11: IADL: Difficulty Making Phone Calls recoding

	IADL: Difficulty Making Phone Calls			Total
	Impaired	Not impaired	Unknown
IADL MAKING PHONE CALLS DIFFICULTY
1	888	0	0	888
5	0	19,782	0	19,782
6	51	0	0	51
7	0	0	146	146
8	0	0	4	4
9	0	0	4	4
Unknown	0	0	37	37
Total	939	19,782	191	20,912

Table 12: IADL: Difficulty Taking Meds recoding

IADL: Difficulty Taking Meds	IADL TAKING MEDICATION DIFFICULTY	IADL TAKING MEDS IF NEEDED DIFFICULTY	n
0	5	NA	19671
0	7	5	202
1	1	NA	923
1	6	NA	24
1	7	1	41
NA	7	8	6
NA	8	NA	5
NA	9	NA	3
NA	NA	NA	37

Table 13: IADL: Difficulty Managing Money Calls recoding

	IADL: Difficulty Managing Money			Total
	Impaired	Not impaired	Unknown
IADL MANAGING MONEY DIFFICULTY
1	1,339	0	0	1,339
5	0	18,751	0	18,751
6	53	0	0	53
7	0	0	708	708
8	0	0	17	17
9	0	0	7	7
Unknown	0	0	37	37
Total	1,392	18,751	769	20,912

Table 14: Descriptive statistics of recoded functional impairment items

Characteristic	N = 20,9121
ADL: Difficulty Dressing
Impaired	2,404 (18%)
Not impaired	10,875 (82%)
Unknown	7,633
ADL: Difficulty Bathing
Impaired	1,729 (17%)
Not impaired	8,552 (83%)
Unknown	10,631
ADL: Difficulty Eating
Impaired	834 (8.1%)
Not impaired	9,439 (92%)
Unknown	10,639
ADL: Difficulty Using Toilet
Impaired	1,368 (13%)
Not impaired	8,892 (87%)
Unknown	10,652
IADL: Difficulty Using Maps
Impaired	2,785 (14%)
Not impaired	16,811 (86%)
Unknown	1,316
IADL: Difficulty Meal Prep
Impaired	1,308 (6.5%)
Not impaired	18,880 (94%)
Unknown	724
IADL: Difficulty Grocery Shopping
Impaired	1,842 (9.1%)
Not impaired	18,324 (91%)
Unknown	746
IADL: Difficulty Making Phone Calls
Impaired	939 (4.5%)
Not impaired	19,782 (95%)
Unknown	191
IADL: Difficulty Taking Meds
Impaired	988 (4.7%)
Not impaired	19,873 (95%)
Unknown	51
IADL: Difficulty Managing Money
Impaired	1,392 (6.9%)
Not impaired	18,751 (93%)
Unknown	769
Sum of ADL/IADL impairments
0	14,821 (71%)
1	2,946 (14%)
2	1,033 (4.9%)
3	651 (3.1%)
4	446 (2.1%)
5	304 (1.5%)
6	244 (1.2%)
7	151 (0.7%)
8	131 (0.6%)
9	99 (0.5%)
10	86 (0.4%)
1 n (%)

Recode the informant based items

This section shows the recoding of the Jorm items and the subjective cognitive impairment item.

There are 16 Jorm items.

• For each Jorm item, three HRS items need to be combined to recreate the Jorm item

The average of these 16 Jorms items is the score

The subjective cognitive impairment item was recoded so that “no change” and “improvement” was combined into one category.

Table 15: Jorm: Remembering things about family

Remembering things about family	RATE R AT REMEMBERING THINGS- PC	ORGANIZATION IMPROVED- PC	ORGANIZATION WORSE- PC	n
1	1	1	NA	8
2	1	2	NA	8
3	2	NA	NA	377
4	3	NA	4	111
5	3	NA	5	213
NA	3	NA	8	2
NA	4	NA	NA	2
NA	NA	NA	NA	20191

Table 16: Jorm: Remembering things that happened recently

Remembering things that happened recently	RATE R AT REMEMBERING RECENT EVENTS- PC	REMEMBERING RECENT EVENTS IMPROVED- PC	REMEMBERING RECENT EVENTS WORSE- PC	n
1	1	1	NA	8
2	1	2	NA	6
3	2	NA	NA	352
4	3	NA	4	116
5	3	NA	5	234
NA	3	NA	8	1
NA	4	NA	NA	3
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 17: Jorm: Recalling conversations a few day later

Recalling conversations a few day later	RATE R AT CONVERSATION RECALL- PC	CONVERSATION RECALL IMPROVED- PC	CONVERSATION RECALL WORSE- PC	n
1	1	1	NA	6
2	1	2	NA	4
3	2	NA	NA	353
4	3	NA	4	124
5	3	NA	5	222
NA	1	9	NA	1
NA	4	NA	NA	10
NA	8	NA	NA	1
NA	NA	NA	NA	20191

Table 18: Jorm: Remembering telephone number

Remembering telephone number	RATE REMEMBERING OWN PHONE NUM- PC	REMEMBERING OWN PHONE NUM IMPROVE- PC	REMEMBERING OWN PHONE NUM WORSE- PC	n
1	1	1	NA	7
2	1	2	NA	4
3	2	NA	NA	425
4	3	NA	4	58
5	3	NA	5	192
NA	4	NA	NA	31
NA	8	NA	NA	3
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 19: Jorm: Remembering day and month

Remembering day and month	RATE REMEMBERING CURRENT DY/MO- PC	REMEMBERING CURRENT DY/MO IMPROVE- PC	REMEMBERING CURRENT DY/MO WORSE- PC	n
1	1	1	NA	5
2	1	2	NA	4
3	2	NA	NA	391
4	3	NA	4	91
5	3	NA	5	206
NA	4	NA	NA	21
NA	8	NA	NA	2
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 20: Jorm: Remembering where things are kept

Remembering where things are kept	RATE REMEMBERING WHERE THINGS KEPT- PC	WHERE THINGS ARE KEPT IMPROVED- PC	WHERE THINGS ARE KEPT WORSE- PC	n
1	1	1	NA	3
2	1	2	NA	4
3	2	NA	NA	415
4	3	NA	4	91
5	3	NA	5	185
NA	4	NA	NA	20
NA	8	NA	NA	1
NA	9	NA	NA	2
NA	NA	NA	NA	20191

Table 21: Jorm: Remembering where to find things

Remembering where to find things	RATE FINDING THINGS IN DIFF PLACES- PC	FINDING THINGS IMPROVED- PC	FINDING THINGS WORSE- PC	n
1	1	1	NA	5
2	1	2	NA	6
3	2	NA	NA	343
4	3	NA	4	113
5	3	NA	5	218
NA	1	9	NA	1
NA	3	NA	8	1
NA	4	NA	NA	29
NA	8	NA	NA	4
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 22: Jorm: Knowing how to work familar machines around the house

Knowing how to work familar machines around the house	RATE WORKING WITH FAMILIAR MACHINES- PC	WORKING WITH FAMILIAR MACHINES IMPR- PC	WORKING WITH FAMILIAR MACHINES WORSE- PC	n
1	1	1	NA	6
2	1	2	NA	6
3	2	NA	NA	371
4	3	NA	4	57
5	3	NA	5	167
NA	4	NA	NA	111
NA	9	NA	NA	3
NA	NA	NA	NA	20191

Table 23: Jorm: Learning to use a new gadget

Learning to use a new gadget	RATE LEARNING NEW MACHINES- PC	LEARNING NEW MACHINES IMPROVED- PC	LEARNING NEW MACHINES WORSE- PC	n
1	1	1	NA	6
2	1	2	NA	8
3	2	NA	NA	317
4	3	NA	4	60
5	3	NA	5	198
NA	4	NA	NA	130
NA	9	NA	NA	2
NA	NA	NA	NA	20191

Table 24: Jorm: Learning new things in general

Learning new things in general	RATE LEARNING NEW THINGS IN GENERAL- PC	LEARNING ABILITY IMPROVE- PC	LEARNING ABILITY WORSE- PC	n
1	1	1	NA	12
2	1	2	NA	9
3	2	NA	4	1
3	2	NA	NA	349
4	3	NA	4	73
5	3	NA	5	214
NA	3	NA	8	1
NA	4	NA	NA	57
NA	8	NA	NA	2
NA	9	NA	NA	3
NA	NA	NA	NA	20191

Table 25: Jorm: Following a story in a book or on TV

Following a story in a book or on TV	RATE ABILITY TO FOLLOW STORY- PC	ABILITY TO FOLLOW STORY IMPROVE- PC	ABILITY TO FOLLOW STORY WORSE- PC	n
1	1	1	NA	13
2	1	2	NA	6
3	2	NA	NA	403
4	3	NA	4	77
5	3	NA	5	175
NA	1	9	NA	1
NA	3	NA	8	1
NA	4	NA	NA	37
NA	8	NA	NA	6
NA	9	NA	NA	2
NA	NA	NA	NA	20191

Table 26: Jorm: Making decisions on everyday matters

Making decisions on everyday matters	RATE MAKING DECISIONS- PC	MAKE DECISIONS IMPROVE- PC	MAKE DECISIONS WORSE- PC	n
1	1	1	NA	7
2	1	2	NA	6
3	2	NA	NA	380
4	3	NA	4	85
5	3	NA	5	186
NA	4	NA	NA	55
NA	9	NA	NA	2
NA	NA	NA	NA	20191

Table 27: Jorm: Handling money for shopping

Handling money for shopping	RATE HANDLING SHOPPING MONEY- PC	HANDLING SHOPPING MONEY IMPROVE- PC	HANDLING SHOPPING MONEY WORSE- PC	n
1	1	1	NA	6
2	1	2	NA	3
3	2	NA	NA	335
4	3	NA	4	37
5	3	NA	5	141
NA	4	NA	NA	198
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 28: Jorm: Handling financial matters

Handling financial matters	RATE HANDLING FINANCES- PC	HANDLING FINANCES IMPROVE- PC	HANDLING FINANCES WORSE- PC	n
1	1	1	NA	5
2	1	2	NA	6
3	2	NA	NA	289
4	3	NA	4	32
5	3	NA	5	139
NA	4	NA	NA	249
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 29: Jorm: Handling everyday arthimetic problems

Handling everyday arthimetic problems	RATE HANDLING DAILY ARITHMETIC PROBS- PC	HANDLING ARITHMETIC PROBLEMS IMPROVE- PC	HANDLING ARITHMETIC PROBLEMS WORSE- PC	n
1	1	1	NA	8
2	1	2	NA	6
3	2	NA	NA	357
4	3	NA	4	47
5	3	NA	5	178
NA	4	NA	NA	122
NA	8	NA	NA	2
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 30: Jorm: Using intelligence to understand what’s going on

Using intelligence to understand what's going on	RATE REASONING- PC	REASONING IMPROVE- PC	REASONING WORSE- PC	n
1	1	1	NA	7
2	1	2	NA	12
3	2	NA	NA	384
4	3	NA	4	92
5	3	NA	5	189
NA	3	NA	8	1
NA	4	NA	NA	32
NA	8	NA	NA	3
NA	9	NA	NA	1
NA	NA	NA	NA	20191

Table 31: Jorm Score

Characteristic	N = 20,912
Jorm score (HRS)
Mean (SD)	3.72 (0.83)
Median (Q1, Q3)	3.38 (3.00, 4.56)
N Non-missing	721
Unknown	20,191
Jorm score (HRS HCAP variable set 3)
Mean (SD)	3.18 (0.55)
Median (Q1, Q3)	3.04 (3.00, 3.31)
N Non-missing	3,496
Unknown	17,416

Figure 1: Distribution of Jorm scores in HRS & HCAP

Figure 2: Scatterplot of Jorm scores by HRS & HCAP

Table 32: Self-concerns

Characteristic	N = 20,9121
Compared to two years ago, would you say your memory is better now, about the same, or worse now than it was then?
Worse	4,315 / 19,938 (22%)
Same/Better	15,623 / 19,938 (78%)
Unknown	974
1 n / N (%)

Recode cognitive items

This section shows the recoding of the cognitive items.

In general, I tried to have higher values indicate correct or better performance.

I recoded “don’t know” and “refusal” to incorrect. These could be changed.

If all the component values of a vd variable are NA, then I set the vd variable to NA.

The tables show the recoded variable on the left column, the original variable in the center, and the sample size on the right column.

Orientation

The number correct of the four orientation to time questions - Month, Day, Year and Day of week.

Table 33: vdori - Orientation to time - number correct recoding

Orientation to Time - number correct	PD151	PD152	PD153	PD154	n
0	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	5. DAY NOT OK	34
0	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	8. DK (Don't Know)	2
0	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	5. DAY NOT OK	6
0	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	8. DK (Don't Know)	1
0	5. MONTH NOT OK	8. DK (Don't Know)	5. YEAR NOT OK	5. DAY NOT OK	1
0	5. MONTH NOT OK	8. DK (Don't Know)	5. YEAR NOT OK	8. DK (Don't Know)	1
0	5. MONTH NOT OK	8. DK (Don't Know)	8. DK (Don't Know)	5. DAY NOT OK	5
0	5. MONTH NOT OK	8. DK (Don't Know)	8. DK (Don't Know)	8. DK (Don't Know)	2
0	8. DK (Don't Know)	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	5. DAY NOT OK	2
0	8. DK (Don't Know)	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	8. DK (Don't Know)	1
0	8. DK (Don't Know)	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	5. DAY NOT OK	1
0	8. DK (Don't Know)	8. DK (Don't Know)	5. YEAR NOT OK	5. DAY NOT OK	4
0	8. DK (Don't Know)	8. DK (Don't Know)	5. YEAR NOT OK	8. DK (Don't Know)	1
0	8. DK (Don't Know)	8. DK (Don't Know)	8. DK (Don't Know)	5. DAY NOT OK	16
0	8. DK (Don't Know)	8. DK (Don't Know)	8. DK (Don't Know)	8. DK (Don't Know)	46
0	9. RF (Refused)	9. RF (Refused)	9. RF (Refused)	5. DAY NOT OK	1
0	9. RF (Refused)	9. RF (Refused)	9. RF (Refused)	9. RF (Refused)	14
1	1. MONTH OK	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	5. DAY NOT OK	35
1	1. MONTH OK	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	8. DK (Don't Know)	1
1	1. MONTH OK	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	5. DAY NOT OK	3
1	1. MONTH OK	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	8. DK (Don't Know)	1
1	1. MONTH OK	5. DAY OF MONTH NOT OK	9. RF (Refused)	5. DAY NOT OK	1
1	1. MONTH OK	8. DK (Don't Know)	5. YEAR NOT OK	5. DAY NOT OK	4
1	1. MONTH OK	8. DK (Don't Know)	5. YEAR NOT OK	8. DK (Don't Know)	3
1	1. MONTH OK	8. DK (Don't Know)	8. DK (Don't Know)	5. DAY NOT OK	3
1	1. MONTH OK	8. DK (Don't Know)	8. DK (Don't Know)	8. DK (Don't Know)	7
1	5. MONTH NOT OK	1. DAY OF MONTH OK	5. YEAR NOT OK	5. DAY NOT OK	5
1	5. MONTH NOT OK	1. DAY OF MONTH OK	8. DK (Don't Know)	8. DK (Don't Know)	2
1	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	1. YEAR OK	5. DAY NOT OK	12
1	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	1. YEAR OK	8. DK (Don't Know)	2
1	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	1. DAY OK	39
1	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	1. DAY OK	4
1	5. MONTH NOT OK	8. DK (Don't Know)	1. YEAR OK	5. DAY NOT OK	1
1	5. MONTH NOT OK	8. DK (Don't Know)	1. YEAR OK	8. DK (Don't Know)	1
1	5. MONTH NOT OK	8. DK (Don't Know)	5. YEAR NOT OK	1. DAY OK	3
1	5. MONTH NOT OK	8. DK (Don't Know)	8. DK (Don't Know)	1. DAY OK	6
1	8. DK (Don't Know)	1. DAY OF MONTH OK	5. YEAR NOT OK	5. DAY NOT OK	1
1	8. DK (Don't Know)	1. DAY OF MONTH OK	8. DK (Don't Know)	8. DK (Don't Know)	1
1	8. DK (Don't Know)	5. DAY OF MONTH NOT OK	1. YEAR OK	8. DK (Don't Know)	1
1	8. DK (Don't Know)	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	1. DAY OK	1
1	8. DK (Don't Know)	8. DK (Don't Know)	1. YEAR OK	5. DAY NOT OK	2
1	8. DK (Don't Know)	8. DK (Don't Know)	1. YEAR OK	8. DK (Don't Know)	2
1	8. DK (Don't Know)	8. DK (Don't Know)	5. YEAR NOT OK	1. DAY OK	4
1	8. DK (Don't Know)	8. DK (Don't Know)	8. DK (Don't Know)	1. DAY OK	26
2	1. MONTH OK	1. DAY OF MONTH OK	5. YEAR NOT OK	5. DAY NOT OK	26
2	1. MONTH OK	1. DAY OF MONTH OK	5. YEAR NOT OK	8. DK (Don't Know)	2
2	1. MONTH OK	1. DAY OF MONTH OK	8. DK (Don't Know)	5. DAY NOT OK	2
2	1. MONTH OK	1. DAY OF MONTH OK	8. DK (Don't Know)	8. DK (Don't Know)	1
2	1. MONTH OK	5. DAY OF MONTH NOT OK	1. YEAR OK	5. DAY NOT OK	99
2	1. MONTH OK	5. DAY OF MONTH NOT OK	1. YEAR OK	8. DK (Don't Know)	10
2	1. MONTH OK	5. DAY OF MONTH NOT OK	5. YEAR NOT OK	1. DAY OK	71
2	1. MONTH OK	5. DAY OF MONTH NOT OK	8. DK (Don't Know)	1. DAY OK	12
2	1. MONTH OK	8. DK (Don't Know)	1. YEAR OK	5. DAY NOT OK	9
2	1. MONTH OK	8. DK (Don't Know)	1. YEAR OK	8. DK (Don't Know)	6
2	1. MONTH OK	8. DK (Don't Know)	5. YEAR NOT OK	1. DAY OK	6
2	1. MONTH OK	8. DK (Don't Know)	8. DK (Don't Know)	1. DAY OK	7
2	5. MONTH NOT OK	1. DAY OF MONTH OK	1. YEAR OK	5. DAY NOT OK	4
2	5. MONTH NOT OK	1. DAY OF MONTH OK	5. YEAR NOT OK	1. DAY OK	10
2	5. MONTH NOT OK	1. DAY OF MONTH OK	8. DK (Don't Know)	1. DAY OK	2
2	5. MONTH NOT OK	5. DAY OF MONTH NOT OK	1. YEAR OK	1. DAY OK	111
2	5. MONTH NOT OK	8. DK (Don't Know)	1. YEAR OK	1. DAY OK	7
2	8. DK (Don't Know)	1. DAY OF MONTH OK	1. YEAR OK	8. DK (Don't Know)	1
2	8. DK (Don't Know)	1. DAY OF MONTH OK	5. YEAR NOT OK	1. DAY OK	3
2	8. DK (Don't Know)	1. DAY OF MONTH OK	8. DK (Don't Know)	1. DAY OK	3
2	8. DK (Don't Know)	5. DAY OF MONTH NOT OK	1. YEAR OK	1. DAY OK	9
2	8. DK (Don't Know)	8. DK (Don't Know)	1. YEAR OK	1. DAY OK	19
3	1. MONTH OK	1. DAY OF MONTH OK	1. YEAR OK	5. DAY NOT OK	189
3	1. MONTH OK	1. DAY OF MONTH OK	1. YEAR OK	8. DK (Don't Know)	15
3	1. MONTH OK	1. DAY OF MONTH OK	5. YEAR NOT OK	1. DAY OK	136
3	1. MONTH OK	1. DAY OF MONTH OK	8. DK (Don't Know)	1. DAY OK	16
3	1. MONTH OK	5. DAY OF MONTH NOT OK	1. YEAR OK	1. DAY OK	1834
3	1. MONTH OK	8. DK (Don't Know)	1. YEAR OK	1. DAY OK	137
3	1. MONTH OK	9. RF (Refused)	1. YEAR OK	1. DAY OK	1
3	5. MONTH NOT OK	1. DAY OF MONTH OK	1. YEAR OK	1. DAY OK	144
3	8. DK (Don't Know)	1. DAY OF MONTH OK	1. YEAR OK	1. DAY OK	9
4	1. MONTH OK	1. DAY OF MONTH OK	1. YEAR OK	1. DAY OK	10490
NA	NA	NA	NA	NA	7212

Table 34: vdori - Orientation to time - number correct

Characteristic	N = 20,9121
Orientation to Time - number correct
0	138 (1.0%)
1	171 (1.2%)
2	420 (3.1%)
3	2,481 (18%)
4	10,490 (77%)
Unknown	7,212
1 n (%)

Count backwards

Table 35: vdcount: Count backwards recoding

Count backwards from 20	PD124	PD129	n
0	5. INCORRECT	NA	1324
0	6. WANTS TO START OVER	5. INCORRECT	11
0	6. WANTS TO START OVER	9. RF (Refused)	2
0	9. RF (Refused)	NA	20
1	1. CORRECT	NA	18519
1	6. WANTS TO START OVER	1. CORRECT	35
NA	NA	NA	1001

Table 36: vdcount: Count backwards

Characteristic	N = 20,9121
Count backwards from 20
Correct	18,554 (93%)
Incorrect	1,357 (6.8%)
Unknown	1,001
1 n (%)

Object naming

The scissors and cactus naming items have been combined into one item.

Table 37: vdlfl2: Scissors & Cactus - number correct recoding

Object naming - Scissors, cactus	PD155	PD156	n
0	5. NOT CORRECT	5. NOT CORRECT	20
0	5. NOT CORRECT	8. DK (Don't Know)	20
0	5. NOT CORRECT	9. RF (Refused)	1
0	8. DK (Don't Know)	5. NOT CORRECT	9
0	8. DK (Don't Know)	8. DK (Don't Know)	27
0	9. RF (Refused)	9. RF (Refused)	17
1	1. SCISSORS OR SHEARS ONLY	5. NOT CORRECT	367
1	1. SCISSORS OR SHEARS ONLY	8. DK (Don't Know)	643
1	1. SCISSORS OR SHEARS ONLY	9. RF (Refused)	4
1	5. NOT CORRECT	1. CACTUS OR NAME OF KIND OF CACTUS	99
1	8. DK (Don't Know)	1. CACTUS OR NAME OF KIND OF CACTUS	28
1	9. RF (Refused)	1. CACTUS OR NAME OF KIND OF CACTUS	3
2	1. SCISSORS OR SHEARS ONLY	1. CACTUS OR NAME OF KIND OF CACTUS	12462
NA	NA	NA	7212

Table 38: vdlfl2: Scissors & Cactus - number correct

Characteristic	N = 20,9121
Object naming - Scissors, cactus
0	94 (0.7%)
1	1,144 (8.4%)
2	12,462 (91%)
Unknown	7,212
1 n (%)

President/Vice-president

The president and vice-president naming items have been combined into one item.

Table 39: vdlfl3: President and vice-president - number correct recoding

Naming - President, Vice-president	PD157	PD158	n
0	5. NOT CORRECT	5. NOT CORRECT	51
0	5. NOT CORRECT	8. DK (Don't Know)	45
0	8. DK (Don't Know)	5. NOT CORRECT	11
0	8. DK (Don't Know)	8. DK (Don't Know)	241
0	8. DK (Don't Know)	9. RF (Refused)	1
0	9. RF (Refused)	8. DK (Don't Know)	4
0	9. RF (Refused)	9. RF (Refused)	23
1	1. LAST NAME CORRECT	5. NOT CORRECT	1343
1	1. LAST NAME CORRECT	8. DK (Don't Know)	3347
1	1. LAST NAME CORRECT	9. RF (Refused)	15
1	5. NOT CORRECT	1. LAST NAME CORRECT	14
1	8. DK (Don't Know)	1. LAST NAME CORRECT	15
1	9. RF (Refused)	1. LAST NAME CORRECT	4
2	1. LAST NAME CORRECT	1. LAST NAME CORRECT	8585
NA	NA	NA	7213

Table 40: vdlfl3: President and vice-president - number correct

Characteristic	N = 20,9121
Naming - President, Vice-president
0	376 (2.7%)
1	4,738 (35%)
2	8,585 (63%)
Unknown	7,213
1 n (%)

Serial 7

The new variable counts the number of correct responses on the serial 7. Each item was checked so that the difference between consecutive items is 7. So if a mistake was made early on, there is an opportunity to get the remaining items correct.

Table 41: PD142-PD146: Serial 7 recoding

Serial 7's - Number correct	rPD142	rPD143	rPD144	rPD145	rPD146	n
0	0. Incorrect	0. Incorrect	0. Incorrect	0. Incorrect	0. Incorrect	406
0	0. Incorrect	0. Incorrect	0. Incorrect	0. Incorrect	NA	43
0	0. Incorrect	0. Incorrect	0. Incorrect	NA	NA	116
0	0. Incorrect	0. Incorrect	NA	NA	NA	266
0	0. Incorrect	NA	NA	NA	NA	1014
1	0. Incorrect	0. Incorrect	0. Incorrect	0. Incorrect	1. Correct	58
1	0. Incorrect	0. Incorrect	0. Incorrect	1. Correct	0. Incorrect	69
1	0. Incorrect	0. Incorrect	1. Correct	0. Incorrect	0. Incorrect	68
1	0. Incorrect	0. Incorrect	1. Correct	0. Incorrect	NA	13
1	0. Incorrect	1. Correct	0. Incorrect	0. Incorrect	0. Incorrect	91
1	0. Incorrect	1. Correct	0. Incorrect	0. Incorrect	NA	14
1	0. Incorrect	1. Correct	0. Incorrect	NA	NA	31
1	1. Correct	0. Incorrect	0. Incorrect	0. Incorrect	0. Incorrect	698
1	1. Correct	0. Incorrect	0. Incorrect	0. Incorrect	NA	82
1	1. Correct	0. Incorrect	0. Incorrect	NA	NA	235
1	1. Correct	0. Incorrect	NA	NA	NA	754
2	0. Incorrect	0. Incorrect	0. Incorrect	1. Correct	1. Correct	36
2	0. Incorrect	0. Incorrect	1. Correct	0. Incorrect	1. Correct	48
2	0. Incorrect	0. Incorrect	1. Correct	1. Correct	0. Incorrect	49
2	0. Incorrect	1. Correct	0. Incorrect	0. Incorrect	1. Correct	24
2	0. Incorrect	1. Correct	0. Incorrect	1. Correct	0. Incorrect	42
2	0. Incorrect	1. Correct	1. Correct	0. Incorrect	0. Incorrect	46
2	0. Incorrect	1. Correct	1. Correct	0. Incorrect	NA	18
2	1. Correct	0. Incorrect	0. Incorrect	0. Incorrect	1. Correct	295
2	1. Correct	0. Incorrect	0. Incorrect	1. Correct	0. Incorrect	266
2	1. Correct	0. Incorrect	1. Correct	0. Incorrect	0. Incorrect	249
2	1. Correct	0. Incorrect	1. Correct	0. Incorrect	NA	52
2	1. Correct	1. Correct	0. Incorrect	0. Incorrect	0. Incorrect	327
2	1. Correct	1. Correct	0. Incorrect	0. Incorrect	NA	67
2	1. Correct	1. Correct	0. Incorrect	NA	NA	179
3	0. Incorrect	0. Incorrect	1. Correct	1. Correct	1. Correct	72
3	0. Incorrect	1. Correct	0. Incorrect	1. Correct	1. Correct	36
3	0. Incorrect	1. Correct	1. Correct	0. Incorrect	1. Correct	26
3	0. Incorrect	1. Correct	1. Correct	1. Correct	0. Incorrect	52
3	1. Correct	0. Incorrect	0. Incorrect	1. Correct	1. Correct	553
3	1. Correct	0. Incorrect	1. Correct	0. Incorrect	1. Correct	274
3	1. Correct	0. Incorrect	1. Correct	1. Correct	0. Incorrect	633
3	1. Correct	1. Correct	0. Incorrect	0. Incorrect	1. Correct	333
3	1. Correct	1. Correct	0. Incorrect	1. Correct	0. Incorrect	389
3	1. Correct	1. Correct	1. Correct	0. Incorrect	0. Incorrect	264
3	1. Correct	1. Correct	1. Correct	0. Incorrect	NA	44
4	0. Incorrect	1. Correct	1. Correct	1. Correct	1. Correct	107
4	1. Correct	0. Incorrect	1. Correct	1. Correct	1. Correct	1417
4	1. Correct	1. Correct	0. Incorrect	1. Correct	1. Correct	871
4	1. Correct	1. Correct	1. Correct	0. Incorrect	1. Correct	493
4	1. Correct	1. Correct	1. Correct	1. Correct	0. Incorrect	851
5	1. Correct	1. Correct	1. Correct	1. Correct	1. Correct	7899
NA	NA	NA	NA	NA	NA	942

Table 42: PD142-PD146: Serial 7

Characteristic	N = 20,9121
Serial 7's - Number correct
0	1,845 (9.2%)
1	2,113 (11%)
2	1,698 (8.5%)
3	2,676 (13%)
4	3,739 (19%)
5	7,899 (40%)
Unknown	942
1 n (%)

Animal naming

This variable is the number of correct responses minus the number of errors. Missing values are set at the median value of errors.

Table 43: PD196: Animal naming recoding

Animal naming (correct - errors)	TOTAL ANIMALS ANSWERS	ANIMAL MISTAKES NUMBER	n
0	0	NA	12
0	1	3	1
0	1	NA	18
0	2	2	1
0	3	3	2
0	6	7	1
0	7	18	1
0	8	15	1
0	8	16	1
0	9	10	2
0	10	10	1
0	10	11	1
0	11	11	1
0	12	15	1
0	13	15	1
0	13	24	1
0	14	14	1
0	14	17	1
0	15	15	1
0	15	22	1
0	16	22	1
0	17	21	1
0	17	24	1
0	17	29	1
0	17	87	1
0	18	20	1
0	18	22	1
0	20	25	1
0	20	37	1
0	21	55	1
0	22	28	1
0	25	38	1
0	25	42	1
0	25	54	1
0	26	42	1
0	26	93	1
0	30	45	1
0	40	45	1
1	2	1	2
1	2	NA	26
1	4	3	1
1	6	5	2
1	9	8	1
1	11	10	1
1	17	16	1
1	20	19	1
2	2	0	1
2	3	1	5
2	3	NA	54
2	4	2	4
2	5	3	2
2	6	4	1
2	7	5	1
2	8	6	1
2	17	15	1
3	4	1	8
3	4	NA	89
3	5	2	6
3	8	5	1
3	9	6	1
3	11	8	1
3	13	10	1
3	23	20	1
3	28	25	1
4	5	1	12
4	5	NA	131
4	6	2	4
4	7	3	5
4	8	4	7
4	9	5	2
4	14	10	1
5	6	1	22
5	6	NA	210
5	7	2	10
5	8	3	3
5	9	4	1
5	10	5	3
5	11	6	4
5	12	7	1
5	13	8	1
5	15	10	1
5	42	37	1
6	6	0	3
6	7	1	36
6	7	NA	258
6	8	2	14
6	9	3	9
6	10	4	3
6	11	5	8
6	12	6	1
6	13	7	1
6	18	12	1
6	24	18	1
7	8	1	51
7	8	NA	303
7	9	2	17
7	10	3	11
7	11	4	3
7	12	5	7
7	13	6	1
7	15	8	1
7	19	12	1
7	24	17	1
8	8	0	2
8	9	1	75
8	9	NA	378
8	10	2	16
8	11	3	17
8	12	4	8
8	13	5	1
8	14	6	2
8	15	7	1
8	16	8	2
8	20	12	1
8	23	15	1
9	9	0	2
9	10	1	56
9	10	NA	368
9	11	2	43
9	12	3	20
9	13	4	12
9	14	5	7
9	17	8	1
9	19	10	1
9	20	11	1
10	10	0	4
10	11	1	86
10	11	NA	447
10	12	2	48
10	13	3	18
10	14	4	8
10	15	5	5
10	16	6	2
10	20	10	2
10	35	25	1
11	11	0	3
11	12	1	128
11	12	98	1
11	12	NA	431
11	13	2	57
11	14	3	16
11	15	4	11
11	16	5	6
11	17	6	3
11	18	7	1
12	12	0	5
12	13	1	130
12	13	NA	436
12	14	2	55
12	15	3	38
12	16	4	20
12	17	5	9
12	18	6	3
12	19	7	1
13	13	0	7
13	14	1	136
13	14	NA	418
13	15	2	72
13	16	3	33
13	17	4	12
13	18	5	9
13	19	6	3
13	20	7	1
13	22	9	1
13	25	12	1
14	14	0	13
14	15	1	150
14	15	NA	455
14	16	2	75
14	17	3	38
14	18	4	16
14	19	5	9
14	20	6	2
14	21	7	1
14	22	8	1
14	24	10	3
15	15	0	8
15	16	1	151
15	16	NA	464
15	17	2	72
15	18	3	29
15	19	4	10
15	20	5	7
15	21	6	3
15	22	7	1
16	16	0	8
16	17	1	149
16	17	98	1
16	17	NA	467
16	18	2	83
16	19	3	29
16	20	4	13
16	21	5	3
16	22	6	3
17	17	0	6
17	18	1	149
17	18	NA	398
17	19	2	76
17	20	3	34
17	21	4	14
17	22	5	6
18	18	0	11
18	19	1	140
18	19	NA	412
18	20	2	79
18	21	3	27
18	22	4	12
18	23	5	4
18	24	6	1
18	26	8	3
18	27	9	1
19	19	0	10
19	20	1	133
19	20	NA	396
19	21	2	79
19	22	3	30
19	23	4	12
19	24	5	4
19	25	6	2
19	31	12	1
20	20	0	8
20	21	1	131
20	21	NA	396
20	22	2	67
20	23	3	19
20	24	4	8
20	25	5	3
20	26	6	2
20	28	8	1
21	21	0	6
21	22	1	100
21	22	NA	347
21	23	2	53
21	24	3	16
21	25	4	8
21	26	5	4
22	22	0	10
22	23	1	100
22	23	NA	295
22	24	2	46
22	25	3	27
22	26	4	7
22	27	5	2
22	36	14	1
23	23	0	7
23	24	1	104
23	24	NA	276
23	25	2	39
23	26	3	14
23	27	4	7
23	28	5	3
23	30	7	1
24	24	0	3
24	25	1	79
24	25	NA	262
24	26	2	43
24	27	3	19
24	28	4	2
24	29	5	2
24	31	7	1
25	25	0	2
25	26	1	64
25	26	NA	187
25	27	2	30
25	28	3	12
25	29	4	8
25	33	8	1
26	26	0	6
26	27	1	58
26	27	NA	180
26	28	2	30
26	29	3	9
26	30	4	3
26	31	5	1
27	27	0	3
27	28	1	45
27	28	NA	131
27	29	2	29
27	30	3	11
27	31	4	4
27	32	5	3
27	33	6	1
27	34	7	1
28	28	0	5
28	29	1	46
28	29	NA	100
28	30	2	17
28	31	3	10
28	32	4	3
28	33	5	1
29	29	0	3
29	30	1	32
29	30	NA	89
29	31	2	11
29	32	3	2
29	33	4	5
29	35	6	1
30	30	0	1
30	31	1	18
30	31	NA	60
30	32	2	11
30	33	3	1
31	31	0	4
31	32	1	15
31	32	NA	42
31	33	2	6
31	34	3	5
31	35	4	2
32	32	0	1
32	33	1	12
32	33	NA	49
32	34	2	8
32	35	3	1
33	33	0	1
33	34	1	14
33	34	NA	41
33	35	2	7
34	35	1	11
34	35	NA	26
34	36	2	1
34	37	3	2
34	39	5	1
35	35	0	1
35	36	1	10
35	36	NA	19
35	37	2	4
35	38	3	1
36	36	0	1
36	37	1	5
36	37	NA	11
36	38	2	3
37	37	0	1
37	38	1	3
37	38	NA	11
37	39	2	2
37	40	3	1
37	41	4	1
38	39	1	2
38	39	NA	7
38	40	2	4
38	42	4	1
39	40	1	2
39	40	NA	5
39	43	4	1
40	41	1	1
40	41	NA	2
40	42	2	1
41	42	NA	3
41	44	3	1
42	43	1	4
42	43	NA	5
43	44	NA	1
43	45	2	1
44	45	NA	1
44	47	3	1
45	46	1	1
45	46	NA	3
46	47	NA	2
47	48	NA	2
49	50	NA	1
51	52	NA	1
56	57	NA	1
86	87	NA	1
88	89	NA	2
NA	NA	NA	7359

Table 44: PD196: Animal naming

Characteristic	N = 20,9121
Animal naming (correct - errors)
0	68 (0.5%)
1	35 (0.3%)
2	70 (0.5%)
3	109 (0.8%)
4	162 (1.2%)
5	257 (1.9%)
6	335 (2.5%)
7	396 (2.9%)
8	504 (3.7%)
9	511 (3.8%)
10	621 (4.6%)
11	657 (4.8%)
12	697 (5.1%)
13	693 (5.1%)
14	763 (5.6%)
15	745 (5.5%)
16	756 (5.6%)
17	683 (5.0%)
18	690 (5.1%)
19	667 (4.9%)
20	635 (4.7%)
21	534 (3.9%)
22	488 (3.6%)
23	451 (3.3%)
24	411 (3.0%)
25	304 (2.2%)
26	287 (2.1%)
27	228 (1.7%)
28	182 (1.3%)
29	143 (1.1%)
30	91 (0.7%)
31	74 (0.5%)
32	71 (0.5%)
33	63 (0.5%)
34	41 (0.3%)
35	35 (0.3%)
36	20 (0.1%)
37	19 (0.1%)
38	14 (0.1%)
39	8 (<0.1%)
40	4 (<0.1%)
41	4 (<0.1%)
42	9 (<0.1%)
43	2 (<0.1%)
44	2 (<0.1%)
45	4 (<0.1%)
46	2 (<0.1%)
47	2 (<0.1%)
49	1 (<0.1%)
51	1 (<0.1%)
56	1 (<0.1%)
86	1 (<0.1%)
88	2 (<0.1%)
Unknown	7,359
1 n (%)

Word recall

I found two variables PD174 and PD184 that are the number of words recalled correctly, so we don’t need to recreate these scores with PD182M1-PD182M13 and PD183M1-PD183M13.

No recoding was done.

Are we going to use both immediate and delayed recall? Or just delayed recall?

Table 45: vdwdimm: Immediate word recall recoding

Word recall - Immediate	NUMBER GOOD - IMMEDIATE	n
0	0	181
1	1	172
2	2	600
3	3	1574
4	4	3321
5	5	4712
6	6	4534
7	7	2912
8	8	1324
9	9	426
10	10	109
NA	NA	1047

Table 46: vdwdimm: Immediate word recall

Characteristic	N = 20,9121
Word recall - Immediate
0	181 (0.9%)
1	172 (0.9%)
2	600 (3.0%)
3	1,574 (7.9%)
4	3,321 (17%)
5	4,712 (24%)
6	4,534 (23%)
7	2,912 (15%)
8	1,324 (6.7%)
9	426 (2.1%)
10	109 (0.5%)
Unknown	1,047
1 n (%)

Table 47: vdwddel: Delayed word recall recoding

Word recall - Delayed	NUMBER GOOD - DELAYED	n
0	0	1255
1	1	815
2	2	1517
3	3	2847
4	4	3986
5	5	4107
6	6	2942
7	7	1472
8	8	627
9	9	234
10	10	62
NA	NA	1048

Table 48: vdwddel: Delayed word recall

Characteristic	N = 20,9121
Word recall - Delayed
0	1,255 (6.3%)
1	815 (4.1%)
2	1,517 (7.6%)
3	2,847 (14%)
4	3,986 (20%)
5	4,107 (21%)
6	2,942 (15%)
7	1,472 (7.4%)
8	627 (3.2%)
9	234 (1.2%)
10	62 (0.3%)
Unknown	1,048
1 n (%)

Numeracy

Recoded 996, 997, and 999 as missing.

Table 49: vdexf7: Number series score recoding

Number series	CALCULATED NUMBER SERIES SCORE	n
409	409	159
413	413	124
429	429	226
435	435	112
462	462	424
465	465	279
484	484	251
485	485	216
488	488	97
489	489	862
501	501	1132
503	503	1069
513	513	1058
514	514	830
518	518	1028
519	519	809
524	524	376
525	525	375
528	528	720
529	529	897
536	536	1254
537	537	1430
546	546	984
547	547	229
549	549	714
558	558	937
567	567	519
570	570	285
584	584	449
NA	996	945
NA	997	915
NA	999	258
NA	NA	949

Table 50: vdexf7: Number series score

Characteristic	N = 20,9121
Number series
409	159 (0.9%)
413	124 (0.7%)
429	226 (1.3%)
435	112 (0.6%)
462	424 (2.4%)
465	279 (1.6%)
484	251 (1.4%)
485	216 (1.2%)
488	97 (0.5%)
489	862 (4.8%)
501	1,132 (6.3%)
503	1,069 (6.0%)
513	1,058 (5.9%)
514	830 (4.7%)
518	1,028 (5.8%)
519	809 (4.5%)
524	376 (2.1%)
525	375 (2.1%)
528	720 (4.0%)
529	897 (5.0%)
536	1,254 (7.0%)
537	1,430 (8.0%)
546	984 (5.5%)
547	229 (1.3%)
549	714 (4.0%)
558	937 (5.3%)
567	519 (2.9%)
570	285 (1.6%)
584	449 (2.5%)
Unknown	3,067
1 n (%)

Rescale cognitive items

The continuous cognitive items were rescaled using the min/max normalization.

Table 51: Descriptive statistics of cognitive items after rescaling

Characteristic	N = 20,912
Animal naming (correct - errors)
N Non-missing	13,553
Mean (SD)	0.18 (0.08)
Median (Q1, Q3)	0.18 (0.13, 0.24)
Min, Max	0.00, 1.00
Unknown	7,359
Word recall - Immediate
N Non-missing	19,865
Mean (SD)	0.53 (0.17)
Median (Q1, Q3)	0.50 (0.40, 0.60)
Min, Max	0.00, 1.00
Unknown	1,047
Word recall - Delayed
N Non-missing	19,864
Mean (SD)	0.43 (0.20)
Median (Q1, Q3)	0.40 (0.30, 0.60)
Min, Max	0.00, 1.00
Unknown	1,048
Number series
N Non-missing	17,845
Mean (SD)	0.64 (0.18)
Median (Q1, Q3)	0.66 (0.54, 0.73)
Min, Max	0.00, 1.00
Unknown	3,067

Model results

A single factor CFA model was fit to the items.

Model 1

The first model used all the items.

The model fit for this model was “poor”.

Table 52: Model 1: Fit statistics

Fit statistic	Value
RMSEA : Estimate	0.087
CFI	0.903
SRMR	0.057

Table 53: Model 1: Factor loadings

Item	Label	Std Factor Loading
vdori	Orientation to time	0.484
vdlfl1z	Animal naming	0.593
vdlfl2	Scissors & cactus	0.661
vdlfl3	President & vice-president	0.581
vdwdimmz	Immediate word recall	0.686
vdwddelz	Delayed word recall	0.681
vdexf7z	Number series	0.613
vdsevens	Serial sevens	0.664
vdcount	Count backwards from 20	0.545

Model 2

The second model removed the immediate word recall item.

The model fit for this model was “good”.

Table 54: Model 2: Fit statistics

Fit statistic	Value
RMSEA : Estimate	0.057
CFI	0.961
SRMR	0.041

Table 55: Model 2: Factor loadings

Item	Label	Std Factor Loading
vdori	Orientation to time	0.485
vdlfl1z	Animal naming	0.591
vdlfl2	Scissors & cactus	0.678
vdlfl3	President & vice-president	0.599
vdwddelz	Delayed word recall	0.589
vdexf7z	Number series	0.642
vdsevens	Serial sevens	0.706
vdcount	Count backwards from 20	0.567

Factor scores

This is the distribution of the factor scores from the final model.

Figure 3: Distribution of factor scores by HCAP inclusion status

Figure 4: Distribution of factor scores by HCAP inclusion status, including HRS sample

Norming the factor score

The HRS sample was filtered to those that were used as the norming sample in HCAP.

The demographic variables were centered to the mean values in the norming sample. The same centering values were used in the full HRS sample.

These are the centering values used:

Table 56: Demographic variable means

variable	mean
female	0.601
black	0.148
hisp	0.100
SCHLYRS	13.329

Summary of the centered demographic variables in the norming sample:

Table 57: Descriptive statistics of centered demographic variables - HCAP

Characteristic	N = 1,7871
Female (centered to HCAP normal sample)
-0.601007274762173	713 / 1,787 (40%)
0.398992725237827	1,074 / 1,787 (60%)
Black (centered to HCAP normal sample)
-0.148293228875209	1,522 / 1,787 (85%)
0.851706771124791	265 / 1,787 (15%)
Hispanic (centered to HCAP normal sample)
-0.0996082820369341	1,609 / 1,787 (90%)
0.900391717963066	178 / 1,787 (10.0%)
School years (centered to HCAP normal sample)	0.00 (2.81)
Unknown	2
1 n / N (%); Mean (SD)

Summary of the centered demographic variables in the full HRS sample:

Table 58: Descriptive statistics of centered demographic variables - HRS

Characteristic	N = 20,9121
Female (centered to HCAP normal sample)
-0.601007274762173	8,664 / 20,909 (41%)
0.398992725237827	12,245 / 20,909 (59%)
Unknown	3
Black (centered to HCAP normal sample)
-0.148293228875209	16,475 / 20,909 (79%)
0.851706771124791	4,434 / 20,909 (21%)
Unknown	3
Hispanic (centered to HCAP normal sample)
-0.0996082820369341	17,482 / 20,909 (84%)
0.900391717963066	3,427 / 20,909 (16%)
Unknown	3
School years (centered to HCAP normal sample)	-0.6 (3.3)
Unknown	91
1 n / N (%); Mean (SD)

Age was modeled with a cubic regression spline. The placement of the knots was determined in the norming sample, using the default percentiles of 5%, 35%, 65%, and 95%. These same knots were used in creating the splines in the full HRS sample.

Theses are the knots used:

 5% 35% 65% 95% 
 65  70  76  85 

To double check the math, these splines were compared to those from the rms::rcs() function.

These are the first 10 rows from the manually created splines.

Table 59: Manually created regression splines

Age spline 1 (from HCAP normal sample)	Age spline 2 (from HCAP normal sample)	Age spline 3 (from HCAP normal sample)
70	0.312	0.000
79	6.710	1.710
79	6.710	1.710
85	15.950	5.400
77	4.314	0.853
77	4.314	0.853
75	2.500	0.312
77	4.314	0.853
69	0.160	0.000
88	20.900	7.425

These are the first 10 rows from the rms::rcs() function.

Table 60: rcs() created regression splines

hrs16_cog_norm	hrs16_cog_norm'	hrs16_cog_norm''
70	0.313	0.000
79	6.710	1.710
79	6.710	1.710
85	15.950	5.400
77	4.314	0.853
77	4.314	0.853
75	2.500	0.313
77	4.314	0.853
69	0.160	0.000
88	20.900	7.425

Factor Score Transformation

The factor score was transformed using a Blom transformation. The transformation was done in the norming sample. Cubic regression splines of the factor score were created to predict the Blom score from the factor score. These cubic regression splines were recreated in the HRS sample to get predicted Blom scores in the HRS sample.

The knots used to create the cubic splines for the Factor score are:

      5%      35%      65%      95% 
-1.18125 -0.09350  0.50775  1.29150 

The regression model is able to predict the blom score well using the factor score cubic splines.

Figure 5: Original Blom score vs Predicted Blom score in norming sample

The cubic splines using the same knots were recreated in the full HRS sample and were used to predict the blom transformed factor score.

Normalization of factor scores

The factor scores were normalized by regressing the predicted blom score on the demographic variables described above in the norming sample. Two way interactions between the variables were included.

Table 61: Parameter estimates of the normalization regression in the norming sample

term	estimate	std.error	statistic	p.value
(Intercept)	0.029	1.182	0.024	0.981
spage1	0.002	0.018	0.123	0.902
spage2	−0.138	0.077	−1.792	0.073
spage3	0.297	0.174	1.709	0.088
cfemale	−3.593	2.358	−1.524	0.128
cblack	−1.828	3.186	−0.574	0.566
chisp	−4.370	3.896	−1.122	0.262
cschlyrs	0.233	0.445	0.523	0.601
spage1:cfemale	0.053	0.035	1.495	0.135
spage1:cblack	0.015	0.048	0.307	0.759
spage1:chisp	0.055	0.058	0.936	0.349
spage1:cschlyrs	−0.001	0.007	−0.143	0.886
spage2:cfemale	−0.194	0.155	−1.257	0.209
spage2:cblack	−0.024	0.214	−0.113	0.910
spage2:chisp	−0.176	0.280	−0.628	0.530
spage2:cschlyrs	0.010	0.030	0.344	0.731
spage3:cfemale	0.421	0.348	1.210	0.226
spage3:cblack	0.023	0.488	0.048	0.962
spage3:chisp	0.344	0.650	0.529	0.597
spage3:cschlyrs	−0.033	0.067	−0.499	0.618
cfemale:cblack	0.160	0.116	1.386	0.166
cfemale:chisp	−0.082	0.139	−0.585	0.559
cfemale:cschlyrs	0.023	0.014	1.622	0.105
cblack:cschlyrs	0.010	0.021	0.472	0.637
chisp:cschlyrs	−0.084	0.018	−4.708	0.000

Plots of observed vs model implied blom scores

These are plots of the observed blom scores versus the model implied blom scores that adjusted for demographic characteristics.

Figure 6: Predicted blom vs normalized blom in norming sample

The regression model was used to estimate the normalized blom scores in the full HRS sample.

Figure 7: Predicted blom vs normalized blom in full HRS sample

Transforming to T-scores

The normalized blom scores were transformed to a T-score using the following equation:

T= 50 + 10 * ((Predicted blom score - Normalized blom score)/(sd(predicted blom score)*sqrt(1-r2)))

The sd of the predicted blom score in the norming sample is: 0.999.

The adjusted model r2 is 0.389 .

Table 62: Summary statistics of the factor score variables in the norming sample

Characteristic	N = 1,7871
Predicted Blom score (model from HCAP normal sample)	0.00 (1.00)
Unknown	11
Adjusted Blom score (from to HCAP normal sample)	0.00 (0.63)
Unknown	2
T-scaled F score (from to HCAP normal sample)	50 (10)
Unknown	13
1 Mean (SD)

Figure 8: Predicted blom score vs T-score in norming sample

Table 63: Summary statistics of the factor score variables in the full HRS sample

Characteristic	N = 20,9121
Predicted Blom score (model from HCAP normal sample)	-0.20 (1.10)
Unknown	942
Adjusted Blom score (from to HCAP normal sample)	-0.16 (0.82)
Unknown	91
T-scaled F score (from to HCAP normal sample)	49 (12)
Unknown	1,030
1 Mean (SD)

Figure 9: Predicted blom score vs T-score in the full HRS sample

Designing the algorithm

This section details how we design the algorithm to mimic the Manly-Jones (2022) algorithm that we implemented in HCAP. In the previous sections we created the components that go into the algorithm - the cognitive factor score, the functional impairment measure, the subjective cognitive complaints indicator. However, these components are different than the ones used in HCAP. So, we need to find cutpoints for these components that give similar prevalence as HCAP.

These are the diagrams of the HCAP algorithm and the proposed HRS algorithm. In both algorithms the fist step is to decide if a participant has normal cognition, mild impairment, or severe impairment. After finding the cutoffs to use to determine cognitive impairment level, the next step is to find cutoffs to determine functional impairment. The functional impairment cutoffs will be determined separately for each level of cognitive impairment.

Figure 10: HCAP algorithm

Figure 11: HRS algorithm

Finding cognitive impairment thresholds

Figure 12 shows the cognitive factor scores are highly correlated between the HCAP cognitive battery and the HRS cognitive battery. The correlation between them is 0.697.

Figure 12: Cognitive factor score in HCAP vs HRS

Figure 13 shows the distribution of the HCAP cognitive factor score by the number of cognitive domains impaired. The plot shows there is good separation in the distribution of the HCAP factor scores by the number of domains impaired.

Figure 13: Distribution of HCAP cognitive factor score by number of domains impaired

Figure 14 is a similar figure that shows the distribution of the HRS cognitive factor score by the number of HCAP cognitive domains impaired. This figure shows there is more overlap of the distributions of the factor scores when separating them by the same variable as in Figure 13.

Figure 14: Distribution of HRS cognitive factor score by number of domains impaired

Table 64 shows that 19.6% had one domain impaired, and 15.7% had two or more domains impaired. So we’ll find cutpoints in the HRS factor score to match these percentages.

Table 64: Distribution of the number of impaired domains in HCAP

Characteristic	N = 2,9931
Number of domains impaired
No domains	1,935 (65%)
1 domain	587 (20%)
2+ domains	471 (16%)
1 n (%)

The values of the HRS factor score that match those percentages are:

     15%      35% 
36.43890 43.77599 

Table 65: Level of cognitive impairment in HRS matched to the number of domains impaired

Characteristic	N = 2,9931
Level of cognitive impairment (HRS)
None	1,941 (67%)
Mild	572 (20%)
Severe	405 (14%)
Unknown	75
1 n (%)

Table 66 shows that the agreement of level of cognitive impairment is high between the two measures. But despite choosing thresholds of the factor score to match the percentages of the number of impaired domains, the agreement is high but not perfect. The weighted kappa is 0.49.

Table 66: Crosstab of number of domains impaired (HCAP) vs matched factor scores (HRS)

	Level of cognitive impairment (HRS)				Total
	None	Mild	Severe	Unknown
Number of domains impaired
No domains	1,528	307	88	12	1,935
1 domain	312	164	99	12	587
2+ domains	101	101	218	51	471
Total	1,941	572	405	75	2,993

Finding functional impairment thresholds

The next step of the algorithm after finding cognitive impairment thresholds is to find thresholds to for functional impairment. This will be done separately by cognitive impairment level.

In the HCAP algorithm, in the 2 or more impaired domains category, 391 / (391 + 211) = 65% had functional impairments. If we use a threshold of 1 or more ADL/IADL impairments then 53.6 % will be categorized as having funcitonal impairment.

Table 67: Distribution of functional impairment among those with severve cognitive impairment

Characteristic	N = 4051
Sum of ADL/IADL impairments
0	188 (46%)
1	97 (24%)
2	36 (8.9%)
3	29 (7.2%)
4	20 (4.9%)
5	8 (2.0%)
6	17 (4.2%)
7	4 (1.0%)
8	1 (0.2%)
9	3 (0.7%)
10	2 (0.5%)
1 n (%)

In the HCAP algorithm, in the 1 impaired domain category, 536 / (536 + 231) = 70% had functional impairments. If we use a threshold of 1 or more ADL/IADL impairments then 35.8 % will be categorized as having funcitonal impairment.

Table 68: Distribution of functional impairment among those with mild cognitive impairment

Characteristic	N = 5721
Sum of ADL/IADL impairments
0	367 (64%)
1	98 (17%)
2	54 (9.4%)
3	20 (3.5%)
4	16 (2.8%)
5	8 (1.4%)
6	6 (1.0%)
7	3 (0.5%)
1 n (%)

Informant based cognitive impairment

Table 69: Crosstab of missing cognitive score and missing informant score

	TF_missing		Total
	0	1
jorm_missing
0	0	71	71
1	2,918	4	2,922
Total	2,918	75	2,993

Apply algorithm in the HCAP sample

In the previous section we found the cutpoints to use to determine cognitive impairment and functional impairment levels. In this section we use those cutpoints to implement the algorithm.

The cutpoints we used for cognitive impairment were less than 36.0 and less than 43.3. The ADL/IADL cutpoints were 1 or more impairments for both of the cognitive levels. The Jorm thresholds were less than 3.0 and less than 3.4.

The following code shows a function that uses these thresholds to create the data file.

algorithm_thresholds

function(df){
  df <- df %>%
    mutate(cog_threshold = case_when(TF <36.0 ~ 2,
                                     TF < 43.3 ~ 1,
                                     !is.na(TF) ~ 0),
           iadl_threshold = case_when(cog_threshold==2 & iadl_imp>0 ~ 1,
                                      cog_threshold==2 & iadl_imp==0 ~ 0,
                                      cog_threshold==1 & iadl_imp>0 ~ 1,
                                      cog_threshold==1 & iadl_imp==0 ~ 0),
           jorm_threshold = case_when(vs3jormsc>= 3.4 ~ 2,
                                      vs3jormsc > 3.0 &  vs3jormsc < 3.4 ~ 1,
                                      vs3jormsc <= 3.0  ~ 0)
    )

  df
}
<bytecode: 0x164cda6d8>

Version 1 of algorithm

Figure 15 shows the first version of the algorithm.

Figure 15: HRS algorithm (v1)

This is the code that creates a function that implements the algortihm shown in Figure 15.

Note: The algorithm uses the Jorm score from HCAP, not the HRS version.

v1_algorithm

function(df) {
  df <- df %>%
    mutate(dx_v1 = case_when(
      cog_threshold == 2 & iadl_threshold == 1 ~ 2,
      cog_threshold == 2 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 2,
      cog_threshold == 2 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 1,
      cog_threshold == 1 & iadl_threshold == 1 ~ 1,
      cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 1,
      cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 0,
      cog_threshold == 0 ~ 0,
      is.na(TF) & jorm_threshold == 2 ~ 2,
      is.na(TF) & jorm_threshold == 1 ~ 1,
      is.na(TF) & jorm_threshold == 0 ~ 0
    ))

  df <- df %>%
    labelled::set_variable_labels(dx_v1 = "Algorithm (V1) Dx") %>%
    labelled::set_value_labels(dx_v1 = c("Normal" = 0, "MCI" = 1, "Dementia" = 2))

  df
}
<bytecode: 0x164d06e10>

Table 70: Relationship between version 1 diagnosis and its constituent components

Algorithm (V1) Dx	cog_threshold	iadl_threshold	Compared to two years ago, would you say your memory is better now, about the same, or worse now than it was then?	jorm_threshold	n
0	0	NA	0	0	927
0	0	NA	0	1	428
0	0	NA	0	2	108
0	0	NA	1	0	228
0	0	NA	1	1	171
0	0	NA	1	2	79
0	1	0	0	0	157
0	1	0	0	1	84
0	1	0	0	2	29
0	NA	NA	0	0	1
0	NA	NA	NA	0	8
1	1	0	1	0	34
1	1	0	1	1	40
1	1	0	1	2	22
1	1	1	0	0	50
1	1	1	0	1	36
1	1	1	0	2	28
1	1	1	1	0	27
1	1	1	1	1	26
1	1	1	1	2	38
1	2	0	0	0	66
1	2	0	0	1	32
1	2	0	0	2	29
1	NA	NA	0	1	3
1	NA	NA	NA	1	12
2	2	0	1	0	18
2	2	0	1	1	18
2	2	0	1	2	25
2	2	1	0	0	49
2	2	1	0	1	26
2	2	1	0	2	62
2	2	1	1	0	8
2	2	1	1	1	23
2	2	1	1	2	48
2	2	1	NA	2	1
2	NA	NA	NA	2	51
NA	1	0	NA	0	1

The following table shows the sample sizes at the places labeled in Figure 15.

Table 71: Sample sizes at various places in the flow diagram

address	n
a1	75
a2	2513
b2	405
b1	24
b3	9
c4	15
c1	51
b4	572
c2	217
c3	188
d1	61
d2	127
c5	205
c6	367
d3	96
d4	270
c7	1941
e1	0
e2	329
e3	443
e4	2220

Table 72: Distribution of dementia diagnoses in HCAP

Characteristic	N = 2,9931
Algorithm (V1) Dx
Normal	2,220 (74%)
MCI	443 (15%)
Dementia	329 (11%)
Unknown	1
HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1)
Normal (1)	2,059 (69%)
MCI (2)	652 (22%)
Dementia (3)	282 (9.4%)
1 n (%)

Table 73: Crosstab of version 1 diagnosis and HCAP diagnosis

	Algorithm (V1) Dx				Total
	Normal	MCI	Dementia	Unknown
HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1)
Normal (1)	1,789	201	68	1	2,059
MCI (2)	374	168	110	0	652
Dementia (3)	57	74	151	0	282
Total	2,220	443	329	1	2,993

$kappa
[1] 0.3390473

$weighted.kappa
[1] 0.5238401

Version 2

In version 2 of the algorithm we removed the self-rated concerns from the severe cognitive impairment portion of the algorithm. Now, if a participant has severe cognitive impairment but no functional impairment, they will be classified as MCI.

Figure 16: HRS algorithm (v2)

This is the code that creates a function that implements the algortihm shown in Figure 16.

v2_algorithm

function(df) {
  df <- df %>%
    mutate(dx_v2 = case_when(
      cog_threshold == 2 & iadl_threshold == 1 ~ 2,
      cog_threshold == 2 & iadl_threshold == 0 ~ 1,
      cog_threshold == 1 & iadl_threshold == 1 ~ 1,
      cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 1 ~ 1,
      cog_threshold == 1 & iadl_threshold == 0 & as.numeric(haven::zap_labels(self_concerns)) == 0 ~ 0,
      cog_threshold == 0 ~ 0,
      is.na(TF) & jorm_threshold == 2 ~ 2,
      is.na(TF) & jorm_threshold == 1 ~ 1,
      is.na(TF) & jorm_threshold == 0 ~ 0
    ))

  df <- df %>%
    labelled::set_variable_labels(dx_v2 = "Algorithm (V2) Dx") %>%
    labelled::set_value_labels(dx_v2 = c("Normal" = 0, "MCI" = 1, "Dementia" = 2))

  df
}
<bytecode: 0x164d642b0>

Table 74: Relationship between version 2 diagnosis and its constituent components

Algorithm (V2) Dx	cog_threshold	iadl_threshold	Compared to two years ago, would you say your memory is better now, about the same, or worse now than it was then?	jorm_threshold	n
0	0	NA	0	0	927
0	0	NA	0	1	428
0	0	NA	0	2	108
0	0	NA	1	0	228
0	0	NA	1	1	171
0	0	NA	1	2	79
0	1	0	0	0	157
0	1	0	0	1	84
0	1	0	0	2	29
0	NA	NA	0	0	1
0	NA	NA	NA	0	8
1	1	0	1	0	34
1	1	0	1	1	40
1	1	0	1	2	22
1	1	1	0	0	50
1	1	1	0	1	36
1	1	1	0	2	28
1	1	1	1	0	27
1	1	1	1	1	26
1	1	1	1	2	38
1	2	0	0	0	66
1	2	0	0	1	32
1	2	0	0	2	29
1	2	0	1	0	18
1	2	0	1	1	18
1	2	0	1	2	25
1	NA	NA	0	1	3
1	NA	NA	NA	1	12
2	2	1	0	0	49
2	2	1	0	1	26
2	2	1	0	2	62
2	2	1	1	0	8
2	2	1	1	1	23
2	2	1	1	2	48
2	2	1	NA	2	1
2	NA	NA	NA	2	51
NA	1	0	NA	0	1

The following table shows the sample sizes at the places labeled in Figure 16.

Table 75: Sample sizes at various places in the v2 flow diagram

address	n
a1	75
a2	2513
b1	405
b2	572
b3	1941
c1	24
c2	9
d1	51
d2	217
d3	188
d4	15
d5	205
d6	367
e1	96
e2	270
f1	0
f2	268
f3	504
f4	2220

Table 76: Distribution of the diagnoses for the subset that was affected by the change in algorithm

Characteristic	N = 611
Algorithm (V1) Dx
Normal	0 (0%)
MCI	0 (0%)
Dementia	61 (100%)
Algorithm (V2) Dx
Normal	0 (0%)
MCI	61 (100%)
Dementia	0 (0%)
HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1)
Normal (1)	15 (25%)
MCI (2)	22 (36%)
Dementia (3)	24 (39%)
1 n (%)

Table 77: Distribution of dementia diagnoses in HCAP

Characteristic	N = 2,9931
Algorithm (V2) Dx
Normal	2,220 (74%)
MCI	504 (17%)
Dementia	268 (9.0%)
Unknown	1
HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1)
Normal (1)	2,059 (69%)
MCI (2)	652 (22%)
Dementia (3)	282 (9.4%)
1 n (%)

Table 78: Crosstab of version 1 diagnosis and HCAP diagnosis

	Algorithm (V1) Dx				Total
	Normal	MCI	Dementia	Unknown
HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1)
Normal (1)	1,789	201	68	1	2,059
MCI (2)	374	168	110	0	652
Dementia (3)	57	74	151	0	282
Total	2,220	443	329	1	2,993

Table 79: Crosstab of version 2 diagnosis and HCAP diagnosis

	Algorithm (V2) Dx				Total
	Normal	MCI	Dementia	Unknown
HRS HCAP Dementia and MCI Classification (EAP version, HRS HCAP variable set 1)
Normal (1)	1,789	216	53	1	2,059
MCI (2)	374	190	88	0	652
Dementia (3)	57	98	127	0	282
Total	2,220	504	268	1	2,993

$kappa
[1] 0.3337944

$weighted.kappa
[1] 0.5156637

Table 80: Crosstab of version 2 diagnosis and version 1 diagnosis

	Algorithm (V2) Dx				Total
	Normal	MCI	Dementia	Unknown
Algorithm (V1) Dx
Normal	2,220	0	0	0	2,220
MCI	0	443	0	0	443
Dementia	0	61	268	0	329
Unknown	0	0	0	1	1
Total	2,220	504	268	1	2,993